Over the past century, numerous methods for assessing cell viability have been developed, and there are many different ways to categorize these methods accordingly. We have chosen to use the Organisation for Economic Co-operation and Development (OECD) classification due to its regulatory importance. The OECD categorizes these methods into four groups: non-invasive cell structure damage, invasive cell structure damage, cell growth, and cellular metabolism. Despite the variety of cell viability methods available, they can all be categorized within these four groups, except for two novel methods based on the cell membrane potential, which we added to the list. Each method operates on different principles and has its own advantages and disadvantages, making it essential for researchers to choose the method that best fits their experimental design. This review aims to assist researchers in making this decision by describing these methods regarding their potential use and providing direct references to the cell viability assessment methods. Additionally, we use the OECD classification to facilitate potential regulatory use and to highlight the need for adding a new category to their list.

The Centers for Disease Control and Prevention (CDC) provides extensive data that indicate our need for drugs to maintain human population health. Despite the substantial availability of drugs on the market, many patients lack specific drugs. New drugs are required to tackle this issue. Moreover, we need more reliable models for testing drug toxicity, as too many drug approval failures occur with the current models. This article briefly describes various approaches of the currently used models for toxicity screening, to justify the selection of in vitro cell-based models. Cell-based toxicity models have the best potential to reliably predict drug toxicity in humans, as they are developed using the cells of the target organism. However, currently, a large gap exists between in vitro cell-based approach to toxicity testing and the clinical approach, which may be contributing to drug approval failures. We propose improvements to in vitro cell-based toxicity models, which is often an insight approach, to better match this approach with the clinical homeostatic approach. This should enable a more accurate comparison of data between the preclinical as well as clinical models and provide a more comprehensive understanding of human physiology and biological effects of drugs.

Determining the viability of cells is fraught with many uncertainties. It is often difficult to determine whether a cell is still alive, approaching the point of no return, or dead. Today, there are many methods for determining cell viability. Most rely on an indirect determination of cell death (metabolism, molecular transport, and leakage, to name a few). In contrast, we have developed a promising novel method for a “direct” determination of cell viability. The potential method assesses cell membrane integrity (which is essential for all viable cells) by measuring the electrical potential of the cell membrane. To test the assay, we chose two different cell types, blood macrophages (TLT) and breast cancer epithelial cells (MCF 7). We exposed them to seven different toxic scenarios (arsenic (V), UV light, hydrogen peroxide, nutrient starvation, Tetrabromobisphenol A, fatty acids, and 5-fluorouracil) to induce different cell death pathways. Under controlled test conditions, the assay showed good accuracy when comparing the toxicity assessment with well-established methods. Moreover, the method showed compatibility with live cell imaging. Although we know that further studies are needed to confirm the performance of the assay in other situations, the results obtained are promising for their wider application in the future.

The persistent gap between preclinical findings and clinical outcomes highlights the limitations of current in vitro models in regulatory toxicology. While New Approach Methodologies (NAMs) promise mechanistic insight, reduced reliance on animal testing, and enhanced human relevance, their translational accuracy remains constrained by oversimplified assumptions. This manuscript identifies foundational aspects of human physiology—degeneracy, interconnected pathways, variability among individuals, biological rhythms, and context dependency—that are often underrepresented in existing systems. I propose reframing biological effects as probabilistic outcomes rather than deterministic events, recognizing that cells and organisms operate through overlapping networks where redundancy, variability, and timing shape the likelihood of specific responses. Building on this framework, I outline a probabilistic and context-dependent cell culture model that integrates viability, functional fidelity, pathway mapping, temporal resolution, and Bayesian inference. Translational relevance is further strengthened by anchoring in vitro measurements to clinically meaningful benchmarks, incorporating patient perspectives, and aligning with regulatory oversight. Although challenges remain—including replicating cell–cell communication, multi-organ synchronization, and harmonizing probabilistic outputs with deterministic regulatory frameworks—embedding these principles into NAMs offers a pathway to overcome the translational bottleneck. By embracing complexity rather than reducing it, NAMs can evolve into tools that are not only mechanistically informative but also predictive, acceptable, and implementable in real-world milieu, ultimately advancing safer and more ethical toxicological assessment.

The existing in vitro toxicological models lack translational potential, which makes difficult the application of gathered information to clinical usage. To tackle this issue, we built a model with four different types of primary liver cells: hepatic sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells and hepatocytes. We cultured them in different combinations of composition and volumes of cell medium, hepatocyte proportions of total cells and additions of extracellular matrixes. We added rifampicin (RIF), ibuprofen (IBU) and 5-fluorouracil (5-FU) to this model and observed the microanatomy and physiology changes for a week with preclinical and clinical instruments. Among the different model configurations, we selected the feature combination of the in vitro model that had similar biomarker values to those measured in clinical diagnostics. When we exposed the selected model configuration to RIF, IBU and 5-FU, we observed similar glucose, triglyceride and albumin dynamics as in vivo (from clinical data). Therefore, we have built an in vitro liver model that resembles the liver microenvironment, and we have analysed it with clinical instrumentation to facilitate data translation. Furthermore, during these observations, we found that Kupffer and LSEC cells are suitable candidates for the search for clinical diagnostic markers of liver function.

The main focus of in vitro toxicity assessment methods is to assess the viability of the cells, which is usually based on metabolism changes. Yet, when exposed to toxic substances, the cell triggers multiple signals in response. With this in mind, we have developed a promising cell-based toxicity method that observes various cell responses when exposed to toxic substances (either death, division, or remain viable). Based on the collective cell response, we observed and predicted the dynamics of the cell population to determine the toxicity of the toxicant. The method was tested with two different conformations: In the first conformation, we exposed a monoculture model of blood macrophages to UV light, hydrogen peroxide, nutrient deprivation, tetrabromobisphenol A, fatty acids, and 5-fluorouracil. In the second, we exposed a coculture liver model consisting of hepatocytes, hepatic stellate cells, Kupffer cells, and liver sinusoidal endothelial cells to rifampicin, ibuprofen, and 5-fluorouracil. The method showed good accuracy compared to established toxicity assessment methods. In addition, this approach provided more representative information on the toxic effects of the compounds, as it considers the different cellular responses induced by toxic agents.